Create handler.py

handler.py

@@ -0,0 +1,36 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftConfig, PeftModel
+import torch.cuda
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        config = PeftConfig.from_pretrained(path)
+        model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
+        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+        # Load the Lora model
+        self.model = PeftModel.from_pretrained(model, path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+            """
+            Args:
+                data (Dict): The payload with the text prompt 
+            and generation parameters.
+            """
+            # Get inputs
+            prompt = data.pop("inputs", None)
+            parameters = data.pop("parameters", None)
+            if prompt is None:
+                raise ValueError("Missing prompt.")
+            # Preprocess
+            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+            # Forward
+            if parameters is not None:
+                output = self.model.generate(input_ids=input_ids, **parameters)
+            else:
+                output = self.model.generate(input_ids=input_ids)
+            # Postprocess
+            prediction = self.tokenizer.decode(output[0])
+            return {"generated_text": prediction}
+